A Methodology for Power Forecasting in Pakistan Using Different Machine Learning Techniques
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Date
2020
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Publisher
UMT, Lahore
Abstract
Over the last decade, the energy sector has experienced a major modernization cycle. Its network
is undergoing accelerated upgrades. The instability of production, demand, and markets is far less
stable than ever before. Also, the corporate concept is profoundly questioned. Many decision-
making processes in this competitive and complex setting depend on probabilistic predictions to
measure unpredictable futures. In recent years, the interest in probabilistic energy forecasting
analysis has rapidly begun, even though many articles in the energy forecasting literature focus on
points or single-valuation forecasting. In Pakistan, the bulk of early studies require various kinds
of econometric modeling. However, the simulation of time series appears to deliver stronger results
given the projected economic and demographic parameters usually deviate from the achievements.
We used machine learning methods, such as ARIMA and Long-Short - Term Memory (LSTM),
to calculate Pakistan's future primary energy demand from 2019 to 2030. In this study, we used
the methods used in machine learning. We have accessed the dataset of the electricity sector for
forecasting purposes from the hydrocarbon development institute of Pakistan (HDIP). The dataset
of HDIP is from 1999 to 2019 with different attributes like Electricity Installed Capacity (Hydel
Thermal (WAPDA), Thermal (K-Electric), Thermal (IPPs), Nuclear), Energy Consumption by
Sector (Domestic, Commercial), Resource Production (Oil, Gas, Coal, Electricity), and Resource
Consumption (Oil, Gas, Coal, Electricity). We have forecast the energy demand of each attribute
till 2030 with ARIMA technique, and LSTM. Predicting overall primary energy demand using
machine learning appears to be more accurate than summing up the individual forecasts. Tests
have shown that specific energy sources exceed annual growth levels